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Hong Yang

Publications and source records attributed to Hong Yang.

At least 55 records · Page 3Linked to original sources

Sufficient conditions for closed-trailable in digraphs

A digraph $D$ with a subset $S$ of $V(D)$ is called $\boldsymbol{S}${\bf -strong} if for every pair of distinct vertices $u$ and $v$ of $S$, there is a $(u, v)$-dipath and a $(v, u)$-dipath in $D$. We define a digraph $D$ with a subset $S$ of $V(D)$ to be $\boldsymbol{S}${\bf -strictly strong} if there exist two nonadjacent vertices $u,v\in S$ such that $D$ contains a closed ditrail through the vertices $u$ and $v$; and define a subset $S\subseteq V(D)$ to be {\bf closed-trailable} if $D$ contains a closed ditrail through all the vertices of $S$. In this paper, we prove that for a digraph $D$ with $n$ vertices and a subset $S$ of $V(D)$, if $D$ is $S$-strong and if $d(u) + d(v)\geq 2n -3$ for any two nonadjacent vertices $u,v$ of $S$, then $S$ is closed-trailable. This result generalizes the theorem of Bang-Jensen et al. \cite{BaMa14} on supereulerianity. Moveover, we show that for a digraph $D$ and a subset $S$ of $V(D)$, if $D$ is $S$-strictly strong and if $δ^0(D\langle S\rangle)\geqα'(D\langle S\rangle)>0$, where $δ^0(D\langle S\rangle)$ is the minimum semi-degree of $D\langle S\rangle$ and $α'(D\langle S\rangle)$ is the matching number of $D\langle S\rangle$, then $S$ is closed-trailable. This result generalizes the theorem of Algefari et al. \cite{AlLa15} on supereulerianity.

math.CO↗

No Analog Combiner TTD-based Hybrid Precoding for Multi-User Sub-THz Communications

We address the design and optimization of real-world-suitable hybrid precoders for multi-user wideband sub-terahertz (sub-THz) communications. We note that the conventional fully connected true-time delay (TTD)-based architecture is impractical because there is no room for the required large number of analog signal combiners in the circuit board. Additionally, analog signal combiners incur significant signal power loss. These limitations are often overlooked in sub-THz research. To overcome these issues, we study a non-overlapping subarray architecture that eliminates the need for analog combiners. We extend the conventional single-user assumption by formulating an optimization problem to maximize the minimum data rate for simultaneously served users. This complex optimization problem is divided into two sub-problems. The first sub-problem aims to ensure a fair subarray allocation for all users and is solved via a continuous domain relaxation technique. The second sub-problem deals with practical TTD device constraints on range and resolution to maximize the subarray gain and is resolved by shifting to the phase domain. Our simulation results highlight significant performance gain for our real-world-ready TTD-based hybrid precoders.

eess.SP↗

Learning Optimal Linear Precoding for Cell-Free Massive MIMO with GNN

We develop a graph neural network (GNN) to compute, within a time budget of 1 to 2 milliseconds required by practical systems, the optimal linear precoder (OLP) maximizing the minimal downlink user data rate for a Cell-Free Massive MIMO system - a key 6G wireless technology. The state-of-the-art method is a bisection search on second order cone programming feasibility test (B-SOCP) which is a magnitude too slow for practical systems. Our approach relies on representing OLP as a node-level prediction task on a graph. We construct a graph that accurately captures the interdependence relation between access points (APs) and user equipments (UEs), and the permutation equivariance of the Max-Min problem. Our neural network, named OLP-GNN, is trained on data obtained by B-SOCP. We tailor the OLP-GNN size, together with several artful data preprocessing and postprocessing methods to meet the runtime requirement. We show by extensive simulations that it achieves near optimal spectral efficiency in a range of scenarios with different number of APs and UEs, and for both line-of-sight and non-line-of-sight radio propagation environments.

eess.SP↗

Local-projective-measurement-enhanced quantum battery capacity

Quantum batteries have significant potential applications for future industry and daily life. The capacity is an important indicator for a battery. Methods to improve the capacity of quantum batteries are important. We consider quantum batteries given by bipartite quantum systems and study the enhancement of the battery capacity under local projective measurements on a subsystem of the quantum state. By using two-qubit Bell-diagonal states and X-type states as examples, we show that quantum battery capacity with respect to the whole system or a subsystem can be improved by local projective measurements. Our theoretical analysis will provide ideas for the experimental development of quantum batteries.

quant-ph↗

JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition

Visual Speech Recognition (VSR) tasks are generally recognized to have a lower theoretical performance ceiling than Automatic Speech Recognition (ASR), owing to the inherent limitations of conveying semantic information visually. To mitigate this challenge, this paper introduces an advanced knowledge distillation approach using a Joint-Embedding Predictive Architecture (JEPA), named JEP-KD, designed to more effectively utilize audio features during model training. Central to JEP-KD is the inclusion of a generative network within the embedding layer, which enhances the video encoder's capacity for semantic feature extraction and brings it into closer alignment with the audio features from a pre-trained ASR model's encoder. This approach aims to progressively reduce the performance gap between VSR and ASR. Moreover, a comprehensive multimodal, multistage training regimen for the JEP-KD framework is established, bolstering the robustness and efficacy of the training process. Experiment results demonstrate that JEP-KD significantly improves the performance of VSR models and demonstrates versatility across different VSR platforms, indicating its potential for broader application within other multimodal tasks.

cs.CV↗

Real-Time Vehicle Detection and Urban Traffic Behavior Analysis Based on UAV Traffic Videos on Mobile Devices

This paper focuses on a real-time vehicle detection and urban traffic behavior analysis system based on Unmanned Aerial Vehicle (UAV) traffic video. By using UAV to collect traffic data and combining the YOLOv8 model and SORT tracking algorithm, the object detection and tracking functions are implemented on the iOS mobile platform. For the problem of traffic data acquisition and analysis, the dynamic computing method is used to process the performance in real time and calculate the micro and macro traffic parameters of the vehicles, and real-time traffic behavior analysis is conducted and visualized. The experiment results reveals that the vehicle object detection can reach 98.27% precision rate and 87.93% recall rate, and the real-time processing capacity is stable at 30 frames per seconds. This work integrates drone technology, iOS development, and deep learning techniques to integrate traffic video acquisition, object detection, object tracking, and traffic behavior analysis functions on mobile devices. It provides new possibilities for lightweight traffic information collection and data analysis, and offers innovative solutions to improve the efficiency of analyzing road traffic conditions and addressing transportation issues for transportation authorities.

cs.CV↗

Sharing Bell nonlocality of bipartite high-dimensional pure states using only projective measurements

Bell nonlocality is the key quantum resource in some device-independent quantum information processing. It is of great importance to study the efficient sharing of this resource. Unsharp measurements are widely used in sharing the nonlocality of an entangled state shared among several sequential observers. Recently, the authors in [Phys. Rev. Lett.129, 230402(2022)] showed that the Bell nonlocality of two-qubit pure states can be shared even when one only uses projective measurements and local randomness. We demonstrate that projective measurements are also sufficient for sharing the Bell nonlocality of arbitrary high-dimensional pure bipartite states. Our results promote further understanding of the nonlocality sharing of high-dimensional quantum states under projective measurements.

quant-ph↗

A GNN Approach for Cell-Free Massive MIMO

Beyond 5G wireless technology Cell-Free Massive MIMO (CFmMIMO) downlink relies on carefully designed precoders and power control to attain uniformly high rate coverage. Many such power control problems can be calculated via second order cone programming (SOCP). In practice, several order of magnitude faster numerical procedure is required because power control has to be rapidly updated to adapt to changing channel conditions. We propose a Graph Neural Network (GNN) based solution to replace SOCP. Specifically, we develop a GNN to obtain downlink max-min power control for a CFmMIMO with maximum ratio transmission (MRT) beamforming. We construct a graph representation of the problem that properly captures the dominant dependence relationship between access points (APs) and user equipments (UEs). We exploit a symmetry property, called permutation equivariance, to attain training simplicity and efficiency. Simulation results show the superiority of our approach in terms of computational complexity, scalability and generalizability for different system sizes and deployment scenarios.

eess.SP↗

Phase transition kinetics revealed by in situ X-ray diffraction in laser-heated dynamic diamond anvil cells

We report on a novel approach to dynamic compression of materials that bridges the gap between previous static- and dynamic- compression techniques, allowing to explore a wide range of pathways in the pressure-temperature space. By combining a dynamic-diamond anvil cell setup with double-sided laser-heating and in situ X-ray diffraction, we are able to perform dynamic compression at high temperature and characterize structural transitions with unprecedented time resolution. Using this method, we investigate the $γ-ε$ phase transition of iron under dynamic compression for the first time, reaching compression rates of hundreds of GPa/s and temperatures of 2000 K. Our results demonstrate a distinct response of the $γ-ε$ and $α-ε$ transitions to the high compression rates achieved. These findings open up new avenues to study tailored dynamic compression pathways in the pressure-temperature space and highlight the potential of this platform to capture kinetic effects in a diamond anvil cell.

cond-mat.mtrl-sci↗

Heterogeneous Graph Neural Architecture Search with GPT-4

Heterogeneous graph neural architecture search (HGNAS) represents a powerful tool for automatically designing effective heterogeneous graph neural networks. However, existing HGNAS algorithms suffer from inefficient searches and unstable results. In this paper, we present a new GPT-4 based HGNAS model to improve the search efficiency and search accuracy of HGNAS. Specifically, we present a new GPT-4 enhanced Heterogeneous Graph Neural Architecture Search (GHGNAS for short). The basic idea of GHGNAS is to design a set of prompts that can guide GPT-4 toward the task of generating new heterogeneous graph neural architectures. By iteratively asking GPT-4 with the prompts, GHGNAS continually validates the accuracy of the generated HGNNs and uses the feedback to further optimize the prompts. Experimental results show that GHGNAS can design new HGNNs by leveraging the powerful generalization capability of GPT-4. Moreover, GHGNAS runs more effectively and stably than previous HGNAS models based on reinforcement learning and differentiable search algorithms.

cs.AI↗

Unidirectional Photonic Reflector Using a Defective Atomic Lattice

Based on the broken spatial symmetry, we propose a novel scheme for engineering a unidirectional photonic reflector using a one-dimensional atomic lattice with defective cells that have been specifically designed to be vacant. By trapping three-level atoms and driving them into the regime of electromagnetically induced transparency, and through the skillful design of the number and position of vacant cells in the lattice, numerical simulations demonstrate that a broad and high unidirectional reflection region can be realized within EIT window. This proposed unidirectional reflector scheme provides a new platform for achieving optical nonreciprocity and has potential applications for designing optical circuits and devices of nonreciprocity at extremely low energy levels.

physics.optics↗

Duality, Criticality, Anomaly, and Topology in Quantum Spin-1 Chains

In quantum spin-1 chains, there is a nonlocal unitary transformation known as the Kennedy-Tasaki transformation $U_{\text{KT}}$, which defines a duality between the Haldane phase and the $\mathbb{Z}_2 \times \mathbb{Z}_2$ symmetry-breaking phase. In this paper, we find that $U_{\text{KT}}$ also defines a duality between a topological Ising critical phase and a trivial Ising critical phase, which provides a "hidden symmetry breaking" interpretation for the topological criticality. Moreover, since the duality relates different phases of matter, we argue that a model with self-duality (i.e., invariant under $U_{\text{KT}}$) is natural to be at a critical or multicritical point. We study concrete examples to demonstrate this argument. In particular, when $H$ is the Hamiltonian of the spin-1 antiferromagnetic Heisenberg chain, we prove that the self-dual model $H + U_{\text{KT}} H U_{\text{KT}}$ is exactly equivalent to a gapless spin-$1/2$ XY chain, which also implies an emergent quantum anomaly. On the other hand, we show that the topological and trivial Ising criticalities that are dual to each other meet at a multicritical point which is indeed self-dual.

cond-mat.str-el↗

Predicted Embedding Power Regression for Large-Scale Out-of-Distribution Detection

Out-of-distribution (OOD) inputs can compromise the performance and safety of real world machine learning systems. While many methods exist for OOD detection and work well on small scale datasets with lower resolution and few classes, few methods have been developed for large-scale OOD detection. Existing large-scale methods generally depend on maximum classification probability, such as the state-of-the-art grouped softmax method. In this work, we develop a novel approach that calculates the probability of the predicted class label based on label distributions learned during the training process. Our method performs better than current state-of-the-art methods with only a negligible increase in compute cost. We evaluate our method against contemporary methods across $14$ datasets and achieve a statistically significant improvement with respect to AUROC (84.2 vs 82.4) and AUPR (96.2 vs 93.7).

cs.CV↗

Smart Hybrid Beamforming and Pilot Assignment for 6G Cell-Free Massive MIMO

This paper investigates Cell-Free massive MIMO networks, where each access point (AP) is equipped with a hybrid transceiver, reducing the complexity and cost compared to a fully digital transceiver. Asymptotic approximations for the spectral efficiency are derived for uplink and downlink. Capitalizing on these expressions, a max-min problem is formulated to optimize the (i) analog beamformer at the APs and (ii) pilot assignment. Simulations show that the optimization of these variables substantially increases the network performance.

cs.IT↗

Investigating $B_s$ three-body decays to scalar mesons in perturbative QCD approach

In this work, we study the branching ratios of B^{0}_{s}\rightarrow a_{0}(980)[ \rightarrow K\overline{K}, πη]a_{0}(980), B^{0}_{s} \rightarrow f_{0}(980)[ \rightarrow π^{+}π^{-}, K^{+}K^{-}]f_{0}(980) and B^{0}_{s} \rightarrow f_{0}(500)[ \rightarrow π^{+}π^{-}]f_{0}(500) decays in the pQCD approach, in which the scalar mesons a_{0}(980), f_{0}(980) and f_{0}(500) are regarded as the lowest-lying q\overline{q} state. In the SU(3) nonet, there exists a mixing between the scalars f_{0}(980) and f_{0}(500), so we have considered the mixing effect in our calculations to obtain more reliable data, and the mixing angle θis set at [15^{\circ}, 82^{\circ}] and [105^{\circ}, 171^{\circ}]. Based on the isospin symmetry, we also estimate the branching ratios of the B^{0}_{s} \rightarrow f_{0}(980)[ \rightarrow π^{0}π^{0}]f_{0}(980) and B^{0}_{s} \rightarrow f_{0}(500)[ \rightarrow π^{0}π^{0}]f_{0}(500) decays. The branching ratios of the B^{0}_{s}\rightarrow a_{0}(980)[ \rightarrow K\overline{K}, πη]a_{0}(980) decays are much small, while the results of the B^{0}_{s} \rightarrow f_{0}(980)[ \rightarrow ππ, K\overline{K}]f_{0}(980) and B^{0}_{s} \rightarrow f_{0}(500)[ \rightarrow ππ]f_{0}(500) decays are at the order of 10^{-6}\sim10^{-5}, which can be tested in the LHCb and Belle II experiments, hopefully.

hep-ph↗

System-environment dynamics of GHZ-like states in noninertial frames

Quantum coherence, quantum entanglement and quantum nonlocality are important resources in quantum information precessing. However, decoherence happens when a quantum system interacts with the external environments. We study the dynamical evolution of the three-qubit GHZ-like states in non-inertial frame when one and/or two qubits undergo decoherence. Under the amplitude damping channel we show that the quantum decoherence and the Unruh effect may have quite different influences on the initial state. Moreover, the genuine tripartite entanglement and the quantum coherence may suffer sudden death during the evolution. The quantum coherence is most resistent to the quantum decoherence and the Unruh effect, then comes the quantum entanglement and the quantum nonlocality which is most fragile among the three. The results provide a new research perspective for relativistic quantum informatics.

quant-ph↗

Lateral quantum confinement regulates charge carrier transfer and biexciton interaction in CdSe/CdSeS core/crown nanoplatelets

Charge carrier dynamics essentially determine the performance of various optoelectronic applications of colloidal semiconductor nanocrystals. Among them, two-dimensional nanoplatelets provide new adjustment freedom for their unique core/crown heterostructure. Herein, we demonstrate that by fine-tuning the core size and the lateral quantum confinement, the charge carrier transfer rate from the crown to the core can be varied by one order of magnitude in CdSe/CdSeS core/alloy-crown nanoplatelets. In addition, the transfer can be affected by a carrier blocking mechanism, i.e., the filled carriers hinder further possible transfer. Furthermore, we found that the biexciton interaction is oppositely affected by quantum confinement and electron delocalization, resulting in a non-monotonic variation of the biexciton binding energy with the emission wavelength. This work provides new observations and insights into the charge carrier transfer dynamics and exciton interactions in colloidal nanoplatelets and will promote their further applications in lasing, display, sensing, etc.

cond-mat.mes-hall↗

A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID

This paper presents the largest publicly available, non-simulated, fleet-wide aircraft flight recording and maintenance log data for use in predicting part failure and maintenance need. We present 31,177 hours of flight data across 28,935 flights, which occur relative to 2,111 unplanned maintenance events clustered into 36 types of maintenance issues. Flights are annotated as before or after maintenance, with some flights occurring on the day of maintenance. Collecting data to evaluate predictive maintenance systems is challenging because it is difficult, dangerous, and unethical to generate data from compromised aircraft. To overcome this, we use the National General Aviation Flight Information Database (NGAFID), which contains flights recorded during regular operation of aircraft, and maintenance logs to construct a part failure dataset. We use a novel framing of Remaining Useful Life (RUL) prediction and consider the probability that the RUL of a part is greater than 2 days. Unlike previous datasets generated with simulations or in laboratory settings, the NGAFID Aviation Maintenance Dataset contains real flight records and maintenance logs from different seasons, weather conditions, pilots, and flight patterns. Additionally, we provide Python code to easily download the dataset and a Colab environment to reproduce our benchmarks on three different models. Our dataset presents a difficult challenge for machine learning researchers and a valuable opportunity to test and develop prognostic health management methods

cs.LG↗